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PM2.5, which is a major source of air pollution, has a considerable impact on human health. In this study, a multi-element joint PM2.5 inversion method based on a deep learning model is proposed. With PM2.5 concentration as the ground truth, 10 elements including the Himawari-AOD daily data products, temperature, relative humidity, and pressure, were introduced as inversion elements. To verify the effectiveness of the method, the experiment was carried out by season using remote sensing data in Eastern China during 2016-2018. The results demonstrate that PM2.5 concentrations were positively correlated with AOD, precipitation, wind speed, and high vegetation cover index and negatively correlated with dwarf vegetation cover index. The correlation with temperature, humidity, pressure, and DEM changed with seasons. Comparative experiments indicated that the accuracy of PM2.5 inversion based on the deep neural network is higher than that of traditional linear and nonlinear models. R2 was above 0.5, and the error was small in each season. The R2 value for autumn, which showed the best inversion, was 0.86, that for summer was 0.75, that for winter was 0.613, and that for spring was 0.566. The visualization of the model illustrates that the inversion result of the DNN model is closer to the PM2.5 concentration distribution interpolated by the ground monitoring station, and the resolution is higher and more accurate.
Liu Lin-Yu, Zhang Yong-Jun, Li Yan-Sheng, Liu Xin-Yi, Wan Yi
Eastern China, Himawari data, PM2.5, deep learning, inversion